Analytical Performance Assessment of Multi-Dimensional Matrix- and Tensor-Based ESPRIT-Type Algorithms

Abstract

In this paper we present a generic framework for the asymptotic performance analysis of subspace-based parameter estimation schemes. It is based on earlier results on an explicit first-order expansion of the estimation error in the signal subspace obtained via an SVD of the noisy observation matrix. We extend these results in a number of aspects. Firstly… (More)
DOI: 10.1109/TSP.2014.2313530

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@article{Roemer2014AnalyticalPA, title={Analytical Performance Assessment of Multi-Dimensional Matrix- and Tensor-Based ESPRIT-Type Algorithms}, author={Florian Roemer and Martin Haardt and Giovanni Del Galdo}, journal={IEEE Transactions on Signal Processing}, year={2014}, volume={62}, pages={2611-2625} }